There is still quite a bit of skepticism in the cog-neuro
community about linguistic representations and their implications for
linguistically dedicated grammar specific nativist components. This skepticism
is largely fuelled, IMO, by associationist-connectionist (AC) prejudices
steeped in a nihilistic Empiricist brew.
Chomsky and Fodor and Gallistel have decisively debunked the relevance
of AC models of cognition, but these ideas are very very very (very…) hard to
dispel. It often seems as if Lila Gleitman was correct when she mooted the
possibility that Empiricism is hard wired in and deeply encapsulated, thus
impervious to empirical refutation. Even as we speak the default view in
cog-neuro is ACish and that there is a general consensus in the cog-neuro
community that the kind of representations that linguists claim to have
discovered just cannot be right for the
simple reason that the brain simply cannot
embody them.
Gallistel and Matzel (see here)
have deftly explored this unholy alliance between associationist psych and
connectionist neuro that anchors the conventional wisdom. Interestingly, this anti
representationalist skepticism is not restricted to the cog-neuro of language.
Indeed, the Empiricist AC view of minds and brains has over the years permeated
work on perception and it has generated skepticism concerning mental (visual) maps
and their cog-neuro legitimacy. This is currently
quite funny for over the last several years Nobel committees have been falling
all over themselves in a rush to award prizes to scientists for the discovery
of neural mental maps. These awards are well deserved, no doubt, but what is
curious is how long it’s taken the cog-neuro community to admit mental maps as
legit hypotheses worthy of recognition.
For a long time, there was quite a bit of excellent behavioral evidence
for their existence, but the combo of associationist dogma linked to Hebbian
neuro made the cog-neuro community skeptical that anything like this could be so. Boy were they wrong and, in
retrospect, boy was this dumb, big time dumb!
Here
is a short popular paper (By Kate
Jeffery) that goes over some of the relevant history. It traces the
resistance to the very idea of mental maps stemming from AC preconceptions.
Interestingly, the resistance was both to the behavioral evidence in favor of
these (the author discusses Tolman’s work in the late 40s. Here’s a quote (5):
Tolman, however, discovered that
rats were able to do things in mazes that they shouldn’t be able to do
according to Behaviourism. They could figure out shortcuts and detours, for
example, even if they hadn’t learned about these. How could they possibly do
this? Tolman was convinced animals must have something like a map in their
brains, which he called a ‘cognitive map’, otherwise their ability to discover
shortcuts would make no sense. Behaviourists were skeptical. Some years later,
when O’Keefe and Nadel laid out in detail why they thought the hippocampus
might be Tolman’s cognitive map, scientists were still skeptical.
Why the resistance? Well ACism prevented conceiving of the
possibility. Here’s how Jeffery put it
(5-6).
One of the difficulties was that
nobody could imagine what a map in the brain would be like. Representing
associations between simple things, such as bells and food, is one thing; but
how to represent places? This seemed to require the mystical unseen
internal ‘black box’ processes (thought and imagination) that Behaviourists had
worked so hard to eradicate from their theories. Opponents of the cognitive map
theory suggested that what place cells reveal about the brain is not a map, so
much as a remarkable capacity to associate together complex sensations such as
images, smells and textures, which all happen to come together at a
place but aren’t in themselves spatial.
Note that the problem was not the absence of evidence for the position. Tolman presented lots
of good evidence. And O’Keefe/Nadel presented more (in fact enough more to get
the Nobel prize for the work). Rather the problem was that none of this made
sense in an AC framework so the Tolman-O’Keefe/Nadel theory just could not be
right, evidence be damned.[1]
What’s the evidence that such maps exist? It involves
finding mental circuits that represent spatial metrics, allowing for the
calculation of metric inferences (where something is and how it is from where
you are). The two kinds of work that have been awarded Nobels involve place
cells and grid cells. The former involve the coding of direction, the latter
coding distance. The article does a nice job of describing what this involves,
so I won’t go into it here. Suffice it
to say, that it appears that Kant (a big deal Rationalist in case you were
wondering) was right on target and we now have good evidence for the existence
of neural circuits that would serve as brain mechanisms for embodying Kant’s
idea that space is a hard wired part of our mental/neural life.
Ok, I cannot resist. Jeffery nicely outlines he challenge
that these discoveries pose for ACism. Here’s another quote concerning grid
cells (the most recent mental map Nobel here)
and how badly it fits with AC dogma (8):[2]
The importance of grid cells lies
in the apparently minor detail that the patches of firing (called ‘firing
fields’) produced by the cells are evenly spaced. That this makes a pretty
pattern is nice, but not so important in itself – what is startling is
that the cell somehow ‘knows’ how far (say) 30 cm is – it must do, or it
wouldn’t be able to fire in correctly spaced places. This even spacing of
firing fields is something that couldn’t possibly have arisen from building up
a web of stimulus associations over the life of the animal, because 30 cm (or
whatever) isn’t an intrinsic property of most environments, and therefore can’t
come through the senses – it must come from inside the rat, through some
distance-measuring capability such as counting footsteps, or measuring the
speed with which the world flows past the senses. In other words, metric
information is inherent in the brain, wired into the grid cells as it were,
regardless of its prior experience. This was a surprising and dramatic
discovery. Studies of other animals, including humans, have revealed place,
head direction and grid cells in these species too, so this seems to be a
general (and thus important) phenomenon and not just a strange quirk of the lab
rat.
As readers of FL know, this is a point that Gallistel and
colleagues have been making for quite a while now and every day the evidence
for neural mechanisms that code for spatial information per se grows stronger. Here is another very recent addition to the
list, one that directly relates to the idea that dead-reckoning involves path
integration. A recent Science paper (here)
reports the discovery of neurons tuned to vector properties. Here’s how the
abstract reports the findings:
To
navigate, animals need to represent not only their own position and
orientation, but also the location of their goal. Neural representations of an
animal’s own position and orientation have been extensively studied. However,
it is unknown how navigational goals are encoded in the brain. We recorded from
hippocampal CA1 neurons of bats flying in complex trajectories toward a spatial
goal. We discovered a subpopulation of neurons with angular tuning to the goal
direction. Many of these neurons were tuned to an occluded goal, suggesting
that goal-direction representation is memory-based. We also found cells that
encoded the distance to the goal, often in conjunction with goal direction. The
goal- direction and goal-distance signals make up a vectorial representation of
spatial goals, suggesting a previously unrecognized neuronal mechanism for
goal-directed navigation.
So, a whole series of neurons tuned to abstracta like place, distance, goal, angle of rotation, and magnitude that plausibly subserve the behavior that has long been noted implicates just such neural circuits. Once again, the neuroscience is finally catching up with the cognitive science. As with parents, the more neuro science matures the smarter classical cognitive science becomes.
Let me emphasize this point, one that Gallistel has forcefully made but is worth repeating at every opportunity until we can cleanly chop off the Empiricist zombie’s head. Cognitive data gets too little respect in the cog-neuro world. But in those areas where real progress has been made, we repeatedly find that the cog theories remain intact even as the neural ones change dramatically. And not only cog-neuro theories. The same holds for the relation of chemistry to physics (as Chomsky noted) and genetics to biochemistry (as Gallistel has observed). It seems that more often than not what needs changing is the substrate theory not the reduced theory. The same scenario is being repeated again in the cog-neuro world. We actually know very little about brain hardware circuitry and we should stop assuming that ACish ideas should be given default status when we consider ways of unifying cognition with neuroscience.
Consider one more interesting paper that hits a Gallistel theme, but from a slightly different angle. I noted that the Science paper found single neurons coding for abstract spatial (vectorial) information. There is another recent bit of work (here) that ran across my desk[3] that is also has a high Gallistel-Intriguing (GI) index.
It appears that slime molds can both acquire info about their environment and can pass this info on to other slime molds. What’s interesting is that these slime molds are unicellular, thus the idea that learning in slime molds amounts to fine tuning a neural net cannot be correct. Thus whatever learning is in this case must be intra, not inter-neural. And this supports the idea that one has intra cellular cognitive computations. Furthermore, when slime molds “fuse” (which they apparently can do, and do do) the information that an informed slime mold has can transfer to its fused partner. This supports the idea that learning can be a function of the changed internal state of a uni-cellular organism.
This is clearly grist for the Gallistel-King conjecture (see here for some discussion) that (some) learning is neuron, not net, based. The arguments that Gallistel has given over the years for this view have been both subtle, abstract and quite arm-chair (and I mean this as a compliment). It seems that as time goes by, more and more data that fits this conception comes in. As Gallistel (and Fodor and Pylyshyn as well) noted, representational accounts prefer certain kinds of computer architectures over others (Turing-von Neumann architectures). These classical computer architectures, we have been told, cannot be what brains exploit. No, brains, we are told repeatedly, use nets and computation is just the Hebb rule with information stored in the strength of the inter-neuronal connections. Moreover, this information is very ACish with abstracta at best emergent, rather than endogenous features of our neural make-up. Well, this seems to be wrong. Dead wrong. And the lesson I draw form all of this is that it will prove wrong for language as well. The sooner we dispense with ACism, the sooner we will start making some serious progress. It’s nothing but a giant impediment, and has proven to be so again and again.
[1]
This is a good place to remind you of the difference between Empiricist and empirical. The latter is responsiveness to evidence. The former is a
theory (which, IMO, given its lack of empirical standing has become little more
than a dogma).
[2]
It strikes me as interesting that this sequence of events reprises what took
place in studies of the immune system. Early theories of antibody formation
were instructionist because how could the body natively code for so many
antibodies? As work progressed, Nobel prizes streamed to those that challenged
this view and proposed selectionist theories wherein the environment selected
from a pre-specified innately generated list of options (see here). It seems that
the less we know, the greater the appeal of environmental conceptions of the
origin of structure (Empiricism being the poster child for this kind of
thinking). As we come to know more, we come to understand how rich is the
contribution of the internal structure of the animal to the problem at hand.
Selectionism and Rationalism go hand in hand. And this appears to be true for
both investigations of the body and the mind.
[3]
Actually, Bill Idsardi feeds me lots of this, so thx Bill.
